Project description

AstroML is a Python module for machine learning and data mining
built on numpy, scipy, scikit-learn, and matplotlib,
and distributed under the 3-Clause BSD license.
It contains a growing library of statistical and machine learning
routines for analyzing astronomical data in python, loaders for several open
astronomical datasets, and a large suite of examples of analyzing and
visualizing astronomical datasets.

This project was started in 2012 by Jake VanderPlas to accompany the book
Statistics, Data Mining, and Machine Learning in Astronomy by
Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.

Core and Addons

The project is split into two components. The core astroML library is
written in python only, and is designed to be very easy to install for
any users, even those who don’t have a working C or fortran compiler.
A companion library, astroML_addons, can be optionally installed for
increased performance on certain algorithms. Every algorithm
in astroML_addons has a pure python counterpart in the
core astroML implementation, but the astroML_addons library
contains faster and more efficient implementations in compiled code.
Furthermore, if astroML_addons is installed on your system, the core
astroML library will import and use the faster routines by default.

The reason for this split is the ease of use for newcomers to Python. If the
prerequisites are already installed on your system, the core astroML
library can be installed and used on any system with little trouble. The
astroML_addons library requires a C compiler, but is also designed to be
easy to install for more advanced users. See further discussion in
“Development”, below.

Installation

This package uses distutils, which is the default way of installing python
modules. Before installation, make sure your system meets the prerequisites
listed in Dependencies, listed below.

Core

To install the core astroML package in your home directory, use:

pip install astroML

The core package is pure python, so installation should be straightforward
on most systems. To install from source, refer to http://github.com/astroML/

Addons

The astroML_addons package requires a working C/C++ compiler for
installation. It can be installed using:

pip install astroML_addons

To install from source, use:

python setup_addons.py install

You can specify an arbitrary directory for installation using:

python setup.py install --prefix='/some/path'

To install system-wide on Linux/Unix systems:

python setup.py build
sudo python setup.py install

Dependencies

There are three levels of dependencies in astroML. Core dependencies are
required for the core astroML package. Add-on dependencies are required
for the performance astroML_addons. Optional dependencies are required
to run some (but not all) of the example scripts. Individual example scripts
will list their optional dependencies at the top of the file.

Core Dependencies

The core astroML package requires the following:

Python version 2.6.x - 2.7.x
(astroML does not yet support python 3.x)

PyMC provides a nice interface for Markov-Chain Monte Carlo. Several astroML
examples use pyMC for exploration of high-dimensional spaces. The examples
were written with pymc version 2.2

HEALPy provides an interface to
the HEALPix pixelization scheme, as well as fast spherical harmonic
transforms.

Development

This package is designed to be a repository for well-written astronomy code,
and submissions of new routines are encouraged. After installing the
version-control system Git, you can check out
the latest sources from GitHub using:

git clone git://github.com/astroML/astroML.git

or if you have write privileges:

git clone git@github.com:astroML/astroML.git

Contribution

We strongly encourage contributions of useful astronomy-related code:
for astroML to be a relevant tool for the python/astronomy community,
it will need to grow with the field of research. There are a few
guidelines for contribution:

General

Any contribution should be done through the github pull request system (for
more information, see the
help page
Code submitted to astroML should conform to a BSD-style license,
and follow the PEP8 style guide.

Documentation and Examples

All submitted code should be documented following the
Numpy Documentation Guide. This is a unified documentation style used
by many packages in the scipy universe.

In addition, it is highly recommended to create example scripts that show the
usefulness of the method on an astronomical dataset (preferably making use
of the loaders in astroML.datasets). These example scripts are in the
examples subdirectory of the main source repository.

Add-on code

We made the decision early-on to separate the core routines from
high-performance compiled routines.
This is to make sure that installation of the core
package is as straightforward as possible (i.e. not requiring a C compiler).

Contributions of efficient compiled code to astroML_addons is encouraged:
the availability of efficient implementations of common algorithms in python
is one of the strongest features of the python universe. The preferred
method of wrapping compiled libraries is to use
cython; other options (weave, SWIG, etc.) are
harder to build and maintain.

Currently, the policy is that any efficient algorithm included in
astroML_addons should have a duplicate python-only implementation in
astroML, with code that selects the faster routine if it’s available.
(For an example of how this works, see the definition of the lomb_scargle
function in astroML/periodogram.py).
This policy exists for two reasons:

it allows novice users to have all the functionality of astroML without
requiring the headache of complicated installation steps.

it serves a didactic purpose: python-only implementations are often easier
to read and understand than equivalent implementations in C or cython.

it enforces the good coding practice of avoiding premature optimization.
First make sure the code works (i.e. write it in simple python). Then
create an optimized version in the addons.

If this policy proves especially burdensome in the future, it may be revisited.